Binary search tree (BST) and hash table (also called hash map) are efficient data structures with unique benefits and limitations. In this Answer, we will explore the capacity of both data structures.
A binary search tree is a data structure where each node stores data (value and optional child references) and has at most two children nodes. These nodes are organized hierarchically, placing smaller values on the left and greater values on the right. Each subtree in a BST is also a BST.
A BST allows fast lookup using binary search, addition, and deletion in logarithmic time complexity.
A binary search tree has many benefits. Some of them are listed below:
We can traverse the tree in both ascending and descending orders.
We can find any element in the logarithmic time.
We can use BST in sorting algorithms, such as tree sort.
A BST can grow and shrink to save space.
Let’s discuss some limitations of using a binary search tree.
A BST can become
A BST doesn’t provide constant time operations.
A hash table is a data structure in which the data is stored on a calculated index. A hash table consists of two parts: a hash function to generate the index and an array to store the values. The data is passed to the hash function to find its index. Selecting an efficient hash function is crucial for creating a good hash table. The hash function should distribute the data evenly to avoid
A hash table’s performance depends on the hash function’s performance. Besides that, a hash table with a low
A hash table offers some unique benefits over other data structures. Some of them are listed below:
A hash table performs basic operations in constant time, such as insertion, deletion, lookup, etc.
We can use hash tables to store different data types since we don’t need to compare values.
We can use hash tables to implement the set data structure.
Let’s discuss some limitations of using a hash table.
A hash table doesn’t maintain the order of the data, which causes the loss of the orderbased operations.
Collisions can occur in a hash table while generating indexes from the hash function. It can decrease the performance of the hash table to linear time.
A hash table needs more memory at creation time to make space for upcoming data items.
Let’s discuss where each data structure is preferable to the other.
A binary search tree can be utilized over a hash table in the following situations:
We can efficiently traverse a tree in different orders (inorder, preorder, postorder, and levelorder), while the hash table doesn’t support this. The binary search tree is the choice if an application requires data traversal.
If the input size is not known in advance, the binary search tree could be a better option to opt for. Otherwise, we can choose a hash table on known input data size for better performance.
A binary search tree can perform the nearest neighbor finding or sorting jobs. It could be the choice while designing such applications.
A hash table is more suitable over a binary search tree in the following scenarios:
We can store, delete, and find data in a hash table in constant time, which makes it superior to a binary search tree. The hash table is the choice if an application requires constant operation time.
If an application requires multiple data types to store and access, the hash table is the choice. Meanwhile, a binary search tree compares values to store and access, which is possible on similar data.
If a cachefriendly application is required, the hash table is the choice because it requires fewer memory reads than a binary search tree. For example, finding a data element in a hash table causes only one memory read, while in a binary search tree, finding a value can cause
Let’s draw a comparison between these two data structures.
Binary Search Tree  Hash Table  
Use Case  Traversal, range, nearest neighbor searches, or sortingbased applications  Applications requiring constanttime operations 
Range Search  Efficient  Require specific implementation 
Maintain Ordering  Yes  No 
Memory Usage/Overhead  Low  High 
Duplicates  Possible  Not possible 
Applicability  Small dataset  Large dataset 
Average Time Complexity 


Input Data Size  No need to know in advance  Required to know for hash table creation 
In this Answer, we’ve explored the binary search tree and hash tables along with their benefits, limitations, and use cases. We’ve seen that the advantage of a hash table is its linear time complexity. It is efficient for applications that need faster operations and don’t have memory issues. On the other hand, if an application has low storage and requires traversal or sorting functionality, the binary search tree is the best choice. We can also utilize the autobalancing functionality of AVL trees for better performance in such applications. Both data structures can be utilized based on the required functionality, such as the .NET Dictionary
type uses a hash table, but the SortedDictionary
type uses a tree.